Computers and other electronic systems are being used in diverse ways to replace operations that traditionally have required intensive human labor. For example, computer controlled robotic systems are becoming commonplace in assembly lines as a labor saving measure. These systems allow companies to control rising costs while providing the same or better quality output. Designers have found that these types of systems are especially effective in performing repetitive processes where the input to the system is known.
Computer or electronics based systems have not been as successful in other areas. For some systems to operate properly, the system must recognize an input from among a wide range of possible inputs. For example, many have experienced the frustration of trying to scan a document into a computer for use in a word processing application. Conventional optical character recognition (OCR) systems used with the scanners to create a word processing document from the scanned image are highly inefficient. A large percentage of the characters are misread by the OCR system, thus requiring a thorough review and editing of the document before it can be used. One contributing cause is that conventional OCR systems do not include an effective "classifier." Other systems similarly cannot be automated because accurate classifiers have not been built with current technology.
A "classifier" is a system that identifies an input by recognizing that the input is a member of one of a number of possible classes. Theoretically, the best type of classifier is a Bayes-Type classifier. A Bayes classifier contains a complete list of all possible inputs and the corresponding classification for each input. For most if not all real world applications, it is not possible to gather samples of all possible inputs to the system. Rather than attempt to build a Bayes Classifier, current systems use a neural network to extrapolate from a small sampling of possible inputs. The neural network typically is trained with the sampling of data and uses various algorithms based on error estimation to classify unknown inputs. Such systems have limited success because the system operates based on error estimation from a small sampling of data.
For the reasons stated above, and for other reasons stated below which will become apparent to those skilled in the art upon reading and understanding the present specification, there is a need in the art for an improved system and method for pattern recognition.